The Influence of Perceived Belonging on Social Network Site Usage



Research on Social Network Sites (SNSs) indicates that all three popular Technology Acceptance Model constructs, Perceived Ease of Use, Perceived Enjoyment, and Perceived Usefulness, influence their Actual System Use. In contrast, little is known about the specific antecedents of Perceived Enjoyment and Perceived Usefulness in an SNS context. We address this gap by studying whether Perceived Belonging, which we describe as the degree to which a person feels connected to and accepted by other individuals, has an influence on these two constructs. After surveying 415 students and applying a structural equation modeling approach, we confirm that Perceived Belonging positively influences both Perceived Enjoyment and Perceived Usefulness and, hence, also indirectly influences overall SNS usage behavior. Overall, our study suggests that SNS service providers have to strongly focus on providing functionalities that enable users to connect and interact with each other in order to achieve an even greater market penetration and maintain a strong growth trajectory.


Behavioral Intention Social Network Site Technology Acceptance Model Standardize Root Mean Square Residual Average Variance Extract 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ajzen, I. (1991): The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), pp. 179–211.CrossRefGoogle Scholar
  2. Alarcón-Del-Amo, M.-D.-C., Lorenzo-Romero, C. and Gómez-Borja, M.-A. (2012): Analysis of acceptance of social networking sites. African Journal of Business Management, 6 (29), pp. 8609–8619.Google Scholar
  3. Bagozzi, R.P. and Yi, Y. (1988): On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16 (1), pp. 74–94.CrossRefGoogle Scholar
  4. Barrera, M. (1986): Distinctions between social support concepts, measures, and models. American Journal of Community Psychology, 14 (4), pp. 413–445.CrossRefGoogle Scholar
  5. Baumeister, R.F. and Leary, M.R. (1995): The need to belong: Desire for interpersonal attachements as a fundamental human motivation. Psychological Bulletin, 117 (3), pp. 497–529.CrossRefGoogle Scholar
  6. Berkman, L.F. and Syme, S.L. (1978): Social networks, host resistance, and mortality: A nine-year follow-up study of alameda county residents. American Journal of Epidemiology, 109 (2), pp. 186–204.Google Scholar
  7. Bonds-Raacke, J. and Raacke, J. (2010): Myspace and facebook: Identifying dimensions of uses and gratifications for friend networking sites. Individual Differences Research, 8 (1), pp. 27–33.Google Scholar
  8. Boyd, D.M. and Ellison, N.B. (2007): Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), pp. 210–230.CrossRefGoogle Scholar
  9. Brief, A.P. and Aldag, R.J. (1977): The intrinsic-extrinsic dichotomy: Toward conceptual clarity. Academy of Management Review, 2 (3), pp. 496–500.Google Scholar
  10. Byrne, B.M. (2001): Structural equation modeling with amos: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  11. Chang, S.-H., Chou, C.-H. and Yang, J.-M. (2010): The literature review of technology acceptance model: A study of the bibliometric distributions. PACIS 2010 Proceedings. Paper 158.Google Scholar
  12. Chesney, T. (2006): An acceptance model for useful and fun information systems. Human Technology, 2 (2), pp. 225–235.Google Scholar
  13. Cobb, S. (1976): Social support as a moderator of life stress. Psychomatic Medicine, 38 (5), pp. 300–314.CrossRefGoogle Scholar
  14. Cohen, S. and Wills, T.A. (1985): Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98 (2), pp. 310–357.CrossRefGoogle Scholar
  15. Davis, F.D. (1989): Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (2), pp. 319–340.CrossRefGoogle Scholar
  16. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989): User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35 (8), pp. 982–1003.CrossRefGoogle Scholar
  17. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1992): Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22 (14), pp. 1111–1132.CrossRefGoogle Scholar
  18. Eaton, W.W. (1978): Life events, social supports, and psychiatric symptoms: A reanalysis of the new haven data. Journal of Health and Social Behavior, 19 (2), pp. 230–234.CrossRefGoogle Scholar
  19. Ernst, C.-P.H., Pfeiffer, J. and Rothlauf, F. (2013a): Hedonic and utilitarian motivations of social network site adoption. Johannes Gutenberg University Mainz: Working Papers in Information Systems and Business Administration. Fornell, C. and Larcker, D.F. (1981): Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), pp. 39–50.Google Scholar
  20. Gangadharbatla, H. (2008): Facebook me: Collective self-esteem, need to belong, and internet self-efficacy as predictors of the igeneration's attitudes toward social networking sites. Journal of Interactive Advertising, 8 (2), pp. 5–15.CrossRefGoogle Scholar
  21. Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2009): Multivariate data analysis. 7th ed., Upper Saddle River, NJ: Prentice Hall.Google Scholar
  22. Hofstede, G. (1983): The cultural relativity of organizational practices and theories. Journal of International Business Studies, 14 (2), pp. 75–89.CrossRefGoogle Scholar
  23. Hu, L.-T. and Bentler, P.M. (1999): Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6 (1), pp. 1–55.CrossRefGoogle Scholar
  24. Hu, T., Poston, R.S. and Kettinger, W.J. (2011): Nonadopters of online social network services: Is it easy to have fun yet? Communications of the Association for Information Systems, 29 (1), pp. 441–458.Google Scholar
  25. Jöreskog, K. and Sörbom, D. (1989): Lisrel 7: User's reference guide. Mooresville, IN: Scientific Software International.Google Scholar
  26. Karahanna, E., Straub, D.W. and Chervany, N.L. (1999): Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23 (2), pp. 183–213.CrossRefGoogle Scholar
  27. Katz, M.L. and Shapiro, C. (1985): Network externalities, competition, and compatibility. The American Economic Review, 75 (3), pp. 424–440.Google Scholar
  28. Krasnova, H., Spiekermann, S., Koroleva, K. and Hildebrand, T. (2010b): Online social networks: Why we disclose. Journal of Information Technology, 25 (2), pp. 109–125.CrossRefGoogle Scholar
  29. Laveist, T.A., Sellers, R.M., Elliot Brown, K.A. and Nickerson, K.J. (1997): Extreme social isolation, use of community-based senior support services, and mortality among african american elderly women. American Journal of Community Psychology, 25 (5), pp. 721–732.CrossRefGoogle Scholar
  30. Leary, M.R., Kelly, K.M., Cottrell, C.A. and Schreindorfer, L.S. (2007): Individual differences in the need to belong: Mapping the nomological network. Duke University: Unpublished manuscript.Google Scholar
  31. Malhotra, N.K., Kim, S.S. and Agarwal, J. (2004): Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model. Information Systems Research, 15 (4), pp. 336–355.CrossRefGoogle Scholar
  32. Maslow, A.H. (1943): A theory of human motivation. Psychological Review, 50 (4), pp. 370–396.CrossRefGoogle Scholar
  33. Moon, J.-W. and Kim, Y.-G. (2001): Extending the tam for a world-wide-web context. Information & Management, 38 (4), pp. 217–230.CrossRefGoogle Scholar
  34. Nunnally, J. (1978): Psychometric theory. 2nd ed., New York, NY: McGraw-Hill.Google Scholar
  35. Raacke, J. and Bonds-Raacke, J. (2008): Myspace and facebook: Applying the uses and gratifications theory to exploring friend-networking sites. CyberPsychology & Behavior, 11 (2), pp. 169–174.CrossRefGoogle Scholar
  36. Rook, K.S. (1984): The negative side of social interactions: Impact on psychological well-being. Journal of Personality and Social Psychology, 46 (5), pp. 1097–1108.CrossRefGoogle Scholar
  37. Sandler, I.N. (1980): Social support resources, stress and maladjustment of poor children. American Journal of Community Psychology, 8 (1), pp. 41–52.CrossRefGoogle Scholar
  38. Sheldon, K.M., Abad, N. and Hinsch, C. (2011): A two-process view of facebook use and relatedness need-satisfaction: Disconnection drives use, and connection rewards it. Journal of Personality and Social Psychology, 100 (4), pp. 766–775.CrossRefGoogle Scholar
  39. Sledgianowski, D. and Kulviwat, S. (2008): Social network sites: Antecedents of user adoption and usage. AMCIS 2008 Proceedings. Paper 83.Google Scholar
  40. Straub, D., Limayem, M. and Karahanna-Evaristo, E. (1995): Measuring system usage: Implications for is theory testing. Management Science, 41 (8), pp. 1328–1342. Subrahmanyam, K., Reich, S.M., Waechter, M. and Espinoza, G. (2008): Online andGoogle Scholar
  41. offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology 29 (6), pp. 420–433.Google Scholar
  42. Sun, H. and Zhang, P. (2006): Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7 (9), pp. 618–645.Google Scholar
  43. Thambusamy, R., Church, M., Nemati, H. and Barrick, J. (2010): Socially exchanging privacy for pleasure: Hedonic use of computer-mediated social networks. ICIS 2010 Proceedings. Paper 253.Google Scholar
  44. Van Der Heijden, H. (2004): User acceptance of hedonic information systems. MIS Quarterly, 28 (4), pp. 695–704.Google Scholar
  45. Venkatesh, V. (2000): Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11 (4), pp. 342–365.CrossRefGoogle Scholar
  46. Venkatesh, V. and Davis, F.D. (2000): A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2), pp. 186–204.CrossRefGoogle Scholar
  47. Venkatesh, V., Speier, C. and Morris, M.G. (2002): User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33 (2), pp. 297–316.CrossRefGoogle Scholar
  48. Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003): User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), pp. 425–478.Google Scholar
  49. Venkatesh, V. and Bala, H. (2008): Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39 (2), pp. 273–315.CrossRefGoogle Scholar
  50. Venkatesh, V., Thong, J.Y.L. and Xu, X. (2012): Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36 (1), pp. 157–178.Google Scholar
  51. Watson, G.B. and Johnson, D. (1972): Social psychology; issues and insights. 2nd ed., Philadelphia, PA: Lippincott.Google Scholar
  52. Yousafzai, S.Y., Foxall, G.R. and Pallister, J.G. (2007): Technology acceptance: A meta-analysis of the tam: Part 2. Journal of Modelling in Management, 2 (3), pp. 281–304.CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  1. 1.Frankfurt am MainGermany

Personalised recommendations